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Condition assessment of high-speed railway track structure based on sparse Bayesian extreme learning machine and Bayesian hypothesis testing
Aiming at condition assessment of ballastless high-speed railway track structures, in this study, a probability prediction model of the structural static responses under temperature loads based on sparse Bayesian extreme learning machine (SBELM) is constructed. Utilizing the probabilistic predictions of the structural static responses, a Bayesian hypothesis testing-based condition assessment method for track structures is proposed. This method is employed for long-term monitoring data analysis of a high-speed railway track structure with a small-radius curve. Implicit mappings between the temperature loads and the structural static responses are obtained by training a SBELM model, and reliable predictions of the subsequent structural static responses based on the monitored temperature data are yielded. Subsequently, the probabilistic predictions of the structural responses are compared with measured data by Bayesian hypothesis testing for effective condition assessment. The illustrative application validates that the proposed method can realize the condition assessment of high-speed railway track structures effectively.
Condition assessment of high-speed railway track structure based on sparse Bayesian extreme learning machine and Bayesian hypothesis testing
Aiming at condition assessment of ballastless high-speed railway track structures, in this study, a probability prediction model of the structural static responses under temperature loads based on sparse Bayesian extreme learning machine (SBELM) is constructed. Utilizing the probabilistic predictions of the structural static responses, a Bayesian hypothesis testing-based condition assessment method for track structures is proposed. This method is employed for long-term monitoring data analysis of a high-speed railway track structure with a small-radius curve. Implicit mappings between the temperature loads and the structural static responses are obtained by training a SBELM model, and reliable predictions of the subsequent structural static responses based on the monitored temperature data are yielded. Subsequently, the probabilistic predictions of the structural responses are compared with measured data by Bayesian hypothesis testing for effective condition assessment. The illustrative application validates that the proposed method can realize the condition assessment of high-speed railway track structures effectively.
Condition assessment of high-speed railway track structure based on sparse Bayesian extreme learning machine and Bayesian hypothesis testing
Wang, Senrong (author) / Gao, Jingze (author) / Lin, Chao (author) / Li, Hui (author) / Huang, Yong (author)
International Journal of Rail Transportation ; 11 ; 364-388
2023-05-04
25 pages
Article (Journal)
Electronic Resource
Unknown
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